MammoClaw: Towards Skill-Evolving Agent Harness for Breast Cancer Mammography Analysis
In this work, we explore MammoClaw, a training-free agent framework that leverages frozen MLLMs for mammography analysis. To support agentic investigation, we equip the agent with lightweight mammography-specific tools for targeted image analysis, including ROI, paired-view, and contralateral-breast examination. MammoClaw iteratively gathers evidence through these tools, while skill evolution enables non-parametric adaptation by transforming failed trajectories into reusable guidance for later runs. We evaluate the framework on BI-RADS assessment and breast density estimation tasks. In our experiments, we find that tools alone do not reliably improve performance, whereas evolved skills ca...
| Authors | Krishna Kanth Nakka |
|---|---|
| Primary category | eess.IV |
| Published | 25 September 2026 |
| Last updated at source | 25 September 2026 |
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